Chen has extensive experience in applying data-intensive science (such as graph theory, machine learning, combinatorial optimisation, workload characterisation, and data analytics) to the design, development, optimisation and operation of large-scale dataflow systems for astronomical data processing and management.
Chen is currently developing the graph-based execution engine to support data processing pipelines for the SKA Science Data Processor (SDP) and the SKA regional centre. He is also the lead author in applying deep learning methods for radio source detection and classification for the Radio Galaxy Zoo citizen science project.
Chen has been working with his colleagues on the development and operation of the Murchison Widefield Array (MWA) data system, which includes data capturing, in-storage processing, long-haul data distribution and long-term archival storage management. His research in this area involves optimal data placement algorithms and disk cache sizing strategies.
At UWA, Chen delivered High Performance Computing and Parallel Programming lectures to Physics Honours students and Machine Learning seminars to ICRAR researchers.
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